According to a global survey among marketing professionals in January 2025, approximately ** percent reported using artificial intelligence (AI) extensively in their data-driven marketing efforts. Around ** percent said they integrated AI in select areas, whereas ** percent were exploring AI, but have not implemented the technology. Some ** percent reported not having plans to use AI.

Introduction

Meta AI Statistics: The year 2024 witnessed Meta Platforms Inc., earlier called Facebook, achieving breakthroughs in AI, new for affecting and giving business experiences. The growth of Meta has thus been greatly supported by the AI pledge that spans advertising strategies to cutting-edge AI model developments. Meta AI, the artificial intelligence division of Meta Platforms, has experienced significant growth and investment in recent years.

As of early 2025, Meta AI boasts over 700 million monthly active users, with projections indicating it could reach 1 billion users within the year. The division's generative AI tools have attracted more than 4 million advertisers, leading to a 70% year-over-year growth in Advantage & shopping campaigns, which now have an annual revenue run rate exceeding USD 20 billion.

This article highlights some key Meta AI statistics and developments, spotlighting the journey in 2024.

The statistic shows the cumulative revenues from the ten leading artificial intelligence (AI) use cases worldwide, between 2016 and 2025. Over the ten years between 2016 and 2025, AI software for vehicular object detection, identification, and avoidance is expected to generate * billion U.S. dollars.

In 2025, a significant number of brand leaders are turning to AI tools to enhance their strategies. Around 77 percent of brands plan to use AI to eliminate guesswork during the discovery process, while about 83 percent aim to leverage AI to improve the overall user experience. With almost 80 percent of brands seeking to differentiate themselves from the competition during discovery.

In 2023, the work tasks related to employee relations presented the highest potential time saving through generative artificial intelligence in organizations worldwide, with an estimate of nine percent of potential saved time. Employee development tasks came in second, with about five percent of time saving.

Global AI usage will skyrocket over the next few years, reaching a potential market value of $190.61 billion by 2025.

A broad dataset providing insights into artificial intelligence statistics and trends for 2025, covering market growth, adoption rates across industries, impacts on employment, AI applications in healthcare, education, and more.

Artificial intelligence has taken over the app world, with thousands of apps integrating AI and the top AI app developers receiving hundred billion dollar valuations. Generative AI, in the form of...

In the first half of 2025, there were slightly more building construction firms than specialty trade contractors using artificial intelligence (AI) in the United States. Nevertheless, less than **** percent of those building construction companies had used AI in the past weeks. That rate was even lower among specialty trade contractors, amounting to **** percent.

AI has already changed and will continue to change the way that we live. These are the latest Artificial Intelligence statistics you need to know.

Introduction

Claude Statistics: Anthropic is a renowned company in the generative-AI world for advanced large language models. Namely, their crown jewel model, Claude, is widely seen as a worthy competitor against the leading AI systems of today. The company was established in 2021 by a troupe of former OpenAI developers and has since created a sharp-talent-based team.

In May 2024, several more former OpenAI employees joined Anthropic, citing safety concerns with their former employer. Major backers include Amazon and Google, the latter of which is developing its own generative AI platform, Gemini.

This article deeply delves into Claude's statistics for 2024, covering its user base, performance indicators, revenue generation, and more.

Introduction

Grammarly AI Statistics: Grammarly, founded in 2009, has evolved into a leading AI-powered writing assistant. As of 2024, the platform boasts over 30 million daily active users and serves more than 70,000 organizations globally. The company has raised $400 million in funding and achieved a valuation of $13 billion. In 2024, Grammarly generated $251.8 million in revenue, reflecting a 40.76% year-over-year growth. The tool is utilized by over 3,000 higher education institutions and is compatible with more than 500,000 applications and websites.

Grammarly's browser extension has been downloaded over 10 million times on Chrome, and its mobile keyboard app has surpassed 10 million downloads on Android. The company employs over 1,000 staff members and operates with a remote-first hybrid model. This article discusses the Grammarly AI Statistics and progressions that report on growth and impact in 2025.

Introduction

Grok AI Statistics: Grok AI is a significant contestant in the fast-growing field of artificial intelligence. Initiated in late 2023 by X, formerly known as Twitter, it offers a unique flavour and a blend of wit, real-time access to data, and advanced reasoning capabilities. In 2024, while generating an expanding user base, Grok AI also launched novel features that helped to set an original tone when measured against ChatGPT from OpenAI and Gemini from Google. This review unveils Grok AI statistics milestones and market opportunities surrounding, accounting for user acquisition, user engagement, and industry positioning.​

As of 2023, the two most popular uses for artificial intelligence (AI) in American and European companies were in process automation and customer or technical service. About ** and ** percent of the respondents claimed to currently use AI for such activities, respectively. A shift is expected to happen in two years, when about ** percent of the surveyed companies forecast to use AI in recruitment and human resources, contrasting with the current ** percent.

Introduction

Perplexity AI statistics: Perplexity AI, founded in August 2022, has rapidly emerged as a formidable player in the AI-assisted search engine sector. By the end of 2024, the company achieved a valuation of USD 9 billion, a significant increase from USD 1 billion in April 2024, following a USD 500 million funding round led by Institutional Venture Partners. The platform's user base expanded to 15 million active users, marking a 50% growth over three months.

In terms of revenue, Perplexity AI reported a revenue run rate of USD 50 million in October 2024, with projections to earn approximately USD 25 million for the year. The company's services have processed over 230 million queries since inception. Perplexity AI's swift ascent underscores its growing influence and competitiveness in the evolving landscape of AI-driven search technologies.

. This article outlines the various Perplexity AI statistics and trends that shaped the growth story throughout 2025.

AI in Healthcare - Quick Overview Statistics

Artificial Intelligence in healthcare refers to the use of advanced computer algorithms and machine learning techniques to analyze data in the healthcare sector to provide better healthcare services.

AI helps healthcare providers make more accurate and real-time diagnoses, personalize treatment plans, and improve patient safety by identifying health risks earlier.

Types of AI Applications in Healthcare Statistics

• Medical imaging analysis
• Natural language processing (NLP)
• Disease prediction and risk assessment
• Virtual Assistants and Chabot’s
• Drug discovery and development
• Robot-assisted surgery
• Patient engagement
• Diagnosis and treatment
• Machine learning

Use of artificial intelligence by businesses and organizations in producing goods or delivering services over the last 12 months, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, second quarter of 2025.

According to Cognitive Market Research, the global Ai Training Data market size is USD 1865.2 million in 2023 and will expand at a compound annual growth rate (CAGR) of 23.50% from 2023 to 2030.

The demand for Ai Training Data is rising due to the rising demand for labelled data and diversification of AI applications.
Demand for Image/Video remains higher in the Ai Training Data market.
The Healthcare category held the highest Ai Training Data market revenue share in 2023.
North American Ai Training Data will continue to lead, whereas the Asia-Pacific Ai Training Data market will experience the most substantial growth until 2030.

Market Dynamics of AI Training Data Market

Key Drivers of AI Training Data Market

Rising Demand for Industry-Specific Datasets to Provide Viable Market Output

A key driver in the AI Training Data market is the escalating demand for industry-specific datasets. As businesses across sectors increasingly adopt AI applications, the need for highly specialized and domain-specific training data becomes critical. Industries such as healthcare, finance, and automotive require datasets that reflect the nuances and complexities unique to their domains. This demand fuels the growth of providers offering curated datasets tailored to specific industries, ensuring that AI models are trained with relevant and representative data, leading to enhanced performance and accuracy in diverse applications.

In July 2021, Amazon and Hugging Face, a provider of open-source natural language processing (NLP) technologies, have collaborated. The objective of this partnership was to accelerate the deployment of sophisticated NLP capabilities while making it easier for businesses to use cutting-edge machine-learning models. Following this partnership, Hugging Face will suggest Amazon Web Services as a cloud service provider for its clients.

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Advancements in Data Labelling Technologies to Propel Market Growth

The continuous advancements in data labelling technologies serve as another significant driver for the AI Training Data market. Efficient and accurate labelling is essential for training robust AI models. Innovations in automated and semi-automated labelling tools, leveraging techniques like computer vision and natural language processing, streamline the data annotation process. These technologies not only improve the speed and scalability of dataset preparation but also contribute to the overall quality and consistency of labelled data. The adoption of advanced labelling solutions addresses industry challenges related to data annotation, driving the market forward amidst the increasing demand for high-quality training data.

In June 2021, Scale AI and MIT Media Lab, a Massachusetts Institute of Technology research centre, began working together. To help doctors treat patients more effectively, this cooperation attempted to utilize ML in healthcare.

www.ncbi.nlm.nih.gov/pmc/articles/PMC7325854/

Restraint Factors Of AI Training Data Market

Data Privacy and Security Concerns to Restrict Market Growth

A significant restraint in the AI Training Data market is the growing concern over data privacy and security. As the demand for diverse and expansive datasets rises, so does the need for sensitive information. However, the collection and utilization of personal or proprietary data raise ethical and privacy issues. Companies and data providers face challenges in ensuring compliance with regulations and safeguarding against unauthorized access or misuse of sensitive information. Addressing these concerns becomes imperative to gain user trust and navigate the evolving landscape of data protection laws, which, in turn, poses a restraint on the smooth progression of the AI Training Data market.

How did COVID–19 impact the Ai Training Data market?

The COVID-19 pandemic has had a multifaceted impact on the AI Training Data market. While the demand for AI solutions has accelerated across industries, the availability and collection of training data faced challenges. The pandemic disrupted traditional data collection methods, leading to a slowdown in the generation of labeled datasets due to restrictions on physical operations. Simultaneously, the surge in remote work and the increased reliance on AI-driven technologies for various applications fueled the need for diverse and relevant training data. This duali...

Compiled survey results on AI usage for resumes, cover letters, and interviews from 7 sources between February 2024 and January 2025.

AI Tool Usage by Indian College Students 2025

This unique dataset, collected via a May 2025 survey, captures how 496 Indian college students use AI tools (e.g., ChatGPT, Gemini, Copilot) in academics. It includes 16 attributes like AI tool usage, trust, impact on grades, and internet access, ideal for education analytics and machine learning.

Columns

• Student_Name: Anonymized student name.
• College_Name: College attended.
• Stream: Academic discipline (e.g., Engineering, Arts).
• Year_of_Study: Year of study (1–4).
• AI_Tools_Used: Tools used (e.g., ChatGPT, Gemini).
• Daily_Usage_Hours: Hours spent daily on AI tools.
• Use_Cases: Purposes (e.g., Assignments, Exam Prep).
• Trust_in_AI_Tools: Trust level (1–5).
• Impact_on_Grades: Grade impact (-3 to +3).
• Do_Professors_Allow_Use: Professor approval (Yes/No).
• Preferred_AI_Tool: Preferred tool.
• Awareness_Level: AI awareness (1–10).
• Willing_to_Pay_for_Access: Willingness to pay (Yes/No).
• State: Indian state.
• Device_Used: Device (e.g., Laptop, Mobile).
• Internet_Access: Access quality (Poor/Medium/High).

Use Cases

• Predict academic performance using AI tool usage.
• Analyze trust in AI across streams or regions.
• Cluster students by usage patterns.
• Study digital divide via Internet_Access.

Source: Collected via Google Forms survey in May 2025, ensuring diverse representation across India. Note: First dataset of its kind on Kaggle!